ArticleData in brief2026
A weighted sentiment dataset for Indonesian telemedicine text classification.
Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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Abstract
The digital era has fundamentally transformed the global healthcare paradigm. The World Health Organization (WHO) defines digital health as the utilization of information and communication technologies (ICT) to enhance the efficiency, accuracy, and accessibility of healthcare services. Consequently, Indonesia's e-health market is projected to reach US$ 3.6 billion by 2031. This growth is driven by rising digital literacy among the population, although infrastructural and regulatory challenges continue to demand serious attention. This phenomenon has generated a massive volume of user review data that contains highly valuable insights into user perceptions, experiences, and overall satisfaction with telemedicine services. Sentiment analysis of these reviews serves as a powerful tool for service providers to gather actionable feedback on their offerings. This paper introduces a novel dataset, named the Indonesian Telemedicine User Review Dataset for Sentiment Analysis. The dataset comprises over 19,680 rows of user reviews sourced from the three most prominent telemedicine applications in Indonesia. Two human annotators annotated the dataset, strictly adhering to guidelines rigorously validated by domain experts in Psychology. Specifically, the dataset contains 14,748 positive reviews, 711 neutral reviews, and 4,221 negative reviews. Ultimately, this dataset serves as a foundational resource to foster future innovations and advancements in sentiment analysis.
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Registered trials
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